stitches in R
IMPORTANT NOTES
- Necessary to avoid R variable names like
variable.namesince.in python is somewhat analogous to$in R. - Follow the instructions in the
stitches-in-R-setupR markdown so that this notebook will work. If you’ve followed that markdown,stitchesshould be installed in ther-reticulatevirtual environment for this notebook and should be callable.
Setup
R
#### R BLOCK ####
library(reticulate)
# # indicate that we want to use a specific virtualenv
use_virtualenv("r-reticulate", required =TRUE)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(tidyr)
library(ggplot2)
# time slice for the map plotting:
map_time <- '2100-12-31'python
#### Python Block ####
import stitches
import pandas as pd
import pkg_resources
import xarray as xr
import numpy as np
pd.set_option('display.max_columns', None)#### Python Block ####
# The CMIP6 ESM we want to emulate and the variables we want to
# emulate
esm = ['CAMS-CSM1-0']
vars1 = ['tas', 'pr']Load Hector data
pulled a quick rcp4.5 from HectorUI
#### R BLOCK ####
hector_tgav <- read.csv('data/Hector-data-2022-09-22.csv', skip=3, stringsAsFactors = FALSE)
hector_exp <- unique(hector_tgav$scenario)
head(hector_tgav)## scenario year variable value units
## 1 RCP Standard-RCP-4.5 1800 Tgav 0.1176974 degC
## 2 RCP Standard-RCP-4.5 1801 Tgav 0.1212491 degC
## 3 RCP Standard-RCP-4.5 1802 Tgav 0.1243894 degC
## 4 RCP Standard-RCP-4.5 1803 Tgav 0.1272345 degC
## 5 RCP Standard-RCP-4.5 1804 Tgav 0.1298589 degC
## 6 RCP Standard-RCP-4.5 1805 Tgav 0.1323124 degCReshape the Hector data to be stitches target
First we reshape the Hector time series to match the CMIP6 ESM GSAT smooth anomaly time series that STITCHES operates on: NOTE we should probably add this as a function to stitches NOTE Hector time series start in 1800 - there’s no science reason we couldn’t do matching over that window and have a gridded netcdf of an extra 50 years of history.
#### R BLOCK ####
hector_tgav %>%
mutate(variable = 'tas',
ensemble = 'hectorUI1',# doesn't affect the matching or stitching
model = 'Hector') %>%
rename(experiment = scenario) %>%
filter(year >= 1850) %>%
select(variable, experiment, ensemble, model, year, value, -units) ->
target_tgav
print(head(target_tgav))## variable experiment ensemble model year value
## 1 tas RCP Standard-RCP-4.5 hectorUI1 Hector 1850 0.08854785
## 2 tas RCP Standard-RCP-4.5 hectorUI1 Hector 1851 0.09338570
## 3 tas RCP Standard-RCP-4.5 hectorUI1 Hector 1852 0.09959573
## 4 tas RCP Standard-RCP-4.5 hectorUI1 Hector 1853 0.10672673
## 5 tas RCP Standard-RCP-4.5 hectorUI1 Hector 1854 0.11386913
## 6 tas RCP Standard-RCP-4.5 hectorUI1 Hector 1855 0.10890346temp<-read.csv("../data/CAMS-CSM1-0_ssp126ave.csv")
target_tgav$value<-NA
target_tgav$value[match(temp$year,target_tgav$year)]<-temp$tgav
#target_tgav <- target_tgav[-251]Convert Hector Tgav to stitches windows
Now that we have shaped it as stitches is expecting, we can use stitches functions to calculate the matching windows for this target data
#### Python Block ####
# smooth out Hector's historic volcano stuff and
# save off the smoothed Hector time series we actually use for matching so can plot later:
target_hector = stitches.fx_processing.calculate_rolling_mean(r.target_tgav, size =31)## /Users/snyd535/.virtualenvs/r-reticulate/lib/python3.9/site-packages/stitches/fx_processing.py:50: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only.
## rslt = rslt.drop('value', 1).rename(columns={'rollingAvg': 'value'}).reset_index(drop=True)target_data = stitches.fx_processing.get_chunk_info(
stitches.fx_processing.chunk_ts(df = target_hector, n=9))
## /Users/snyd535/.virtualenvs/r-reticulate/lib/python3.9/site-packages/stitches/fx_processing.py:121: FutureWarning: Passing a set as an indexer is deprecated and will raise in a future version. Use a list instead.
## extra_info = df[extra_columns].drop_duplicates()
## /Users/snyd535/.virtualenvs/r-reticulate/lib/python3.9/site-packages/stitches/fx_processing.py:163: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
## fx_dx_info = fx_dx_info.append(row)
## /Users/snyd535/.virtualenvs/r-reticulate/lib/python3.9/site-packages/stitches/fx_processing.py:163: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
## fx_dx_info = fx_dx_info.append(row)
## /Users/snyd535/.virtualenvs/r-reticulate/lib/python3.9/site-packages/stitches/fx_processing.py:163: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
## fx_dx_info = fx_dx_info.append(row)
## /Users/snyd535/.virtualenvs/r-reticulate/lib/python3.9/site-packages/stitches/fx_processing.py:163: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
## fx_dx_info = fx_dx_info.append(row)
## /Users/snyd535/.virtualenvs/r-reticulate/lib/python3.9/site-packages/stitches/fx_processing.py:163: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
## fx_dx_info = fx_dx_info.append(row)
## /Users/snyd535/.virtualenvs/r-reticulate/lib/python3.9/site-packages/stitches/fx_processing.py:163: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
## fx_dx_info = fx_dx_info.append(row)
## /Users/snyd535/.virtualenvs/r-reticulate/lib/python3.9/site-packages/stitches/fx_processing.py:163: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
## fx_dx_info = fx_dx_info.append(row)
## /Users/snyd535/.virtualenvs/r-reticulate/lib/python3.9/site-packages/stitches/fx_processing.py:163: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
## fx_dx_info = fx_dx_info.append(row)
## /Users/snyd535/.virtualenvs/r-reticulate/lib/python3.9/site-packages/stitches/fx_processing.py:163: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
## fx_dx_info = fx_dx_info.append(row)
## /Users/snyd535/.virtualenvs/r-reticulate/lib/python3.9/site-packages/stitches/fx_processing.py:163: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
## fx_dx_info = fx_dx_info.append(row)
## /Users/snyd535/.virtualenvs/r-reticulate/lib/python3.9/site-packages/stitches/fx_processing.py:163: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
## fx_dx_info = fx_dx_info.append(row)
## /Users/snyd535/.virtualenvs/r-reticulate/lib/python3.9/site-packages/stitches/fx_processing.py:163: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
## fx_dx_info = fx_dx_info.append(row)
## /Users/snyd535/.virtualenvs/r-reticulate/lib/python3.9/site-packages/stitches/fx_processing.py:163: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
## fx_dx_info = fx_dx_info.append(row)
## /Users/snyd535/.virtualenvs/r-reticulate/lib/python3.9/site-packages/stitches/fx_processing.py:163: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
## fx_dx_info = fx_dx_info.append(row)
## /Users/snyd535/.virtualenvs/r-reticulate/lib/python3.9/site-packages/stitches/fx_processing.py:163: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
## fx_dx_info = fx_dx_info.append(row)
## /Users/snyd535/.virtualenvs/r-reticulate/lib/python3.9/site-packages/stitches/fx_processing.py:163: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
## fx_dx_info = fx_dx_info.append(row)
## /Users/snyd535/.virtualenvs/r-reticulate/lib/python3.9/site-packages/stitches/fx_processing.py:163: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
## fx_dx_info = fx_dx_info.append(row)
## /Users/snyd535/.virtualenvs/r-reticulate/lib/python3.9/site-packages/stitches/fx_processing.py:163: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
## fx_dx_info = fx_dx_info.append(row)
## /Users/snyd535/.virtualenvs/r-reticulate/lib/python3.9/site-packages/stitches/fx_processing.py:163: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
## fx_dx_info = fx_dx_info.append(row)
## /Users/snyd535/.virtualenvs/r-reticulate/lib/python3.9/site-packages/stitches/fx_processing.py:163: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
## fx_dx_info = fx_dx_info.append(row)
## /Users/snyd535/.virtualenvs/r-reticulate/lib/python3.9/site-packages/stitches/fx_processing.py:163: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
## fx_dx_info = fx_dx_info.append(row)
## /Users/snyd535/.virtualenvs/r-reticulate/lib/python3.9/site-packages/stitches/fx_processing.py:163: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
## fx_dx_info = fx_dx_info.append(row)
## /Users/snyd535/.virtualenvs/r-reticulate/lib/python3.9/site-packages/stitches/fx_processing.py:163: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
## fx_dx_info = fx_dx_info.append(row)
## /Users/snyd535/.virtualenvs/r-reticulate/lib/python3.9/site-packages/stitches/fx_processing.py:163: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
## fx_dx_info = fx_dx_info.append(row)
## /Users/snyd535/.virtualenvs/r-reticulate/lib/python3.9/site-packages/stitches/fx_processing.py:163: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
## fx_dx_info = fx_dx_info.append(row)
## /Users/snyd535/.virtualenvs/r-reticulate/lib/python3.9/site-packages/stitches/fx_processing.py:163: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
## fx_dx_info = fx_dx_info.append(row)
## /Users/snyd535/.virtualenvs/r-reticulate/lib/python3.9/site-packages/stitches/fx_processing.py:163: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
## fx_dx_info = fx_dx_info.append(row)
## /Users/snyd535/.virtualenvs/r-reticulate/lib/python3.9/site-packages/stitches/fx_processing.py:163: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
## fx_dx_info = fx_dx_info.append(row)
## /Users/snyd535/.virtualenvs/r-reticulate/lib/python3.9/site-packages/stitches/fx_processing.py:171: SettingWithCopyWarning:
## A value is trying to be set on a copy of a slice from a DataFrame.
## Try using .loc[row_indexer,col_indexer] = value instead
##
## See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
## extra_info["i"] = 1print(target_data.head())## ensemble experiment model variable start_yr end_yr year \
## 0 hectorUI1 RCP Standard-RCP-4.5 Hector tas 1850 1858 1854
## 1 hectorUI1 RCP Standard-RCP-4.5 Hector tas 1859 1867 1863
## 2 hectorUI1 RCP Standard-RCP-4.5 Hector tas 1868 1876 1872
## 3 hectorUI1 RCP Standard-RCP-4.5 Hector tas 1877 1885 1881
## 4 hectorUI1 RCP Standard-RCP-4.5 Hector tas 1886 1894 1890
##
## fx dx
## 0 -0.602934 -0.005883
## 1 -0.621072 -0.001074
## 2 -0.671851 -0.008360
## 3 -0.689557 0.003447
## 4 -0.670346 -0.001765Load the stitches archive data for matching
## ensemble variable model experiment start_yr end_yr year \
## 4501 r1i1p1f1 tas CAMS-CSM1-0 ssp126 1850 1858 1854
## 4502 r1i1p1f1 tas CAMS-CSM1-0 ssp126 1859 1867 1863
## 4503 r1i1p1f1 tas CAMS-CSM1-0 ssp126 1868 1876 1872
## 4504 r1i1p1f1 tas CAMS-CSM1-0 ssp126 1877 1885 1881
## 4505 r1i1p1f1 tas CAMS-CSM1-0 ssp126 1886 1894 1890
##
## fx dx
## 4501 -0.459767 -0.002050
## 4502 -0.624444 -0.031290
## 4503 -0.769748 0.004645
## 4504 -0.762718 -0.012289
## 4505 -0.710355 0.030490Match
#### python block ####
my_recipes = stitches.make_recipe(target_data, archive_data,
non_tas_variables=['pr'],
tol = 0.14, N_matches = 10000,
reproducible = True)## You have requested more recipes than possible for at least one target trajectories, returning what canprint(my_recipes.head())## target_start_yr target_end_yr archive_experiment archive_variable \
## 0 1850 1858 historical tas
## 1 1859 1867 historical tas
## 2 1868 1876 historical tas
## 3 1877 1885 historical tas
## 4 1886 1894 historical tas
##
## archive_model archive_ensemble stitching_id \
## 0 CAMS-CSM1-0 r2i1p1f1 RCP Standard-RCP-4.5~hectorUI1~1
## 1 CAMS-CSM1-0 r2i1p1f1 RCP Standard-RCP-4.5~hectorUI1~1
## 2 CAMS-CSM1-0 r1i1p1f1 RCP Standard-RCP-4.5~hectorUI1~1
## 3 CAMS-CSM1-0 r2i1p1f1 RCP Standard-RCP-4.5~hectorUI1~1
## 4 CAMS-CSM1-0 r1i1p1f1 RCP Standard-RCP-4.5~hectorUI1~1
##
## archive_start_yr archive_end_yr \
## 0 1895 1903
## 1 1886 1894
## 2 1895 1903
## 3 1904 1912
## 4 1913 1921
##
## tas_file \
## 0 gs://cmip6/CMIP6/CMIP/CAMS/CAMS-CSM1-0/histori...
## 1 gs://cmip6/CMIP6/CMIP/CAMS/CAMS-CSM1-0/histori...
## 2 gs://cmip6/CMIP6/CMIP/CAMS/CAMS-CSM1-0/histori...
## 3 gs://cmip6/CMIP6/CMIP/CAMS/CAMS-CSM1-0/histori...
## 4 gs://cmip6/CMIP6/CMIP/CAMS/CAMS-CSM1-0/histori...
##
## pr_file
## 0 gs://cmip6/CMIP6/CMIP/CAMS/CAMS-CSM1-0/histori...
## 1 gs://cmip6/CMIP6/CMIP/CAMS/CAMS-CSM1-0/histori...
## 2 gs://cmip6/CMIP6/CMIP/CAMS/CAMS-CSM1-0/histori...
## 3 gs://cmip6/CMIP6/CMIP/CAMS/CAMS-CSM1-0/histori...
## 4 gs://cmip6/CMIP6/CMIP/CAMS/CAMS-CSM1-0/histori...Unless otherwise specified by the argument
reproducible = True, the draws stitches takes from the pool
of matches is stochastic.
Within each of these ensembles, there’s no envelope collapse. If we were to concatenate them together into a super-ensemble, there would be. So it’s not as powerful as a very large ensemble of collapse free realizations but it’s still a distinct ensemble to consider.
stitch Tgav
#### python block ####
stitched_global_temp = stitches.gmat_stitching(my_recipes)## /Users/snyd535/.virtualenvs/r-reticulate/lib/python3.9/site-packages/stitches/fx_stitch.py:344: FutureWarning: In a future version of pandas, a length 1 tuple will be returned when iterating over a groupby with a grouper equal to a list of length 1. Don't supply a list with a single grouper to avoid this warning.
## for name, match in rp.groupby(['stitching_id']):print(stitched_global_temp.head())## year value variable stitching_id
## 0 1850 -0.748826 tas RCP Standard-RCP-4.5~hectorUI1~1
## 1 1851 -0.581701 tas RCP Standard-RCP-4.5~hectorUI1~1
## 2 1852 -0.474767 tas RCP Standard-RCP-4.5~hectorUI1~1
## 3 1853 -0.585308 tas RCP Standard-RCP-4.5~hectorUI1~1
## 4 1854 -0.473484 tas RCP Standard-RCP-4.5~hectorUI1~1plot Tgav
## Warning in matrix(stitched_tgav$value, length(1850:2100), 5): data length [1004]
## is not a sub-multiple or multiple of the number of columns [5]
stitch gridded
Using the above recipes, stitches can produce new gridded netcdf files for each recipe, for multiple variables. Creating multiple netcdfs of monthly data is slower than focusing on GSAT. Still much faster than an ESM, and once the recipes are set, very parallelizable. But for tractability, we are just going to stitch the gridded netcdfs for one generated ensemble member.
#### python block ####
# Just do one realization
recipe = my_recipes[my_recipes['stitching_id'] == 'RCP Standard-RCP-4.5~hectorUI1~1'].reset_index(drop=True).copy()We must specify a directory for the netcdfs to be saved to. We will use simply do it directly here. On my computer, constructing these 4 netcdfs took ~2-3 minutes.
#### python block ####
stitches.gridded_stitching(out_dir='data', rp =recipe)## ['Stitching gridded netcdf for: CAMS-CSM1-0 tas RCP Standard-RCP-4.5~hectorUI1~1']
## ['data/stitched_CAMS-CSM1-0_tas_RCP Standard-RCP-4.5~hectorUI1~1.nc', 'data/stitched_CAMS-CSM1-0_pr_RCP Standard-RCP-4.5~hectorUI1~1.nc']plot gridded setup
- we will do maps of all 4 variables at a specified month and year
- we will do a time series of each variable in a specific grid cell
## [1] "data/stitched_CAMS-CSM1-0_pr_RCP Standard-RCP-4.5~hectorUI1~1.nc"
## [2] "data/stitched_CAMS-CSM1-0_tas_RCP Standard-RCP-4.5~hectorUI1~1.nc"It’s a lot easier to use python to load in the netcdfs and select a grid cell or a time slice, save those off as simpler data frames than a full netcdf, and plot in R. The reshaping set of code that we often do in R to work with netcdf data is just so much faster if we’ve pre-sliced the data to a single grid cell or single time step in python.
tas
#### python block ####
[tas_time_slice, tas_grid_slice] = select_time_grid_slice(varname='tas')Take a look at this data in R and plot it
## <xarray.DataArray 'tas' (lat: 160, lon: 320)>
## array([[267.88116, 267.91257, 267.8655 , ..., 267.8967 , 267.88245, 267.85605],
## [266.10693, 265.94617, 266.01797, ..., 266.0147 , 266.2696 , 265.87897],
## [266.76355, 266.97217, 266.93915, ..., 266.85504, 266.90277, 266.82892],
## ...,
## [242.1192 , 242.14171, 242.16599, ..., 242.10185, 242.1081 , 242.09921],
## [241.67508, 241.69434, 241.70775, ..., 241.60387, 241.62997, 241.6528 ],
## [241.35718, 241.37509, 241.39268, ..., 241.33455, 241.34186, 241.34927]],
## dtype=float32)
## Coordinates:
## time datetime64[ns] 2100-12-31
## * lat (lat) float64 -89.14 -88.03 -86.91 -85.79 ... 86.91 88.03 89.14
## * lon (lon) float64 0.0 1.125 2.25 3.375 4.5 ... 355.5 356.6 357.8 358.9
## Attributes:
## units: K
## variable: tas
## experiment: historical
## ensemble: r2i1p1f1
## model: CAMS-CSM1-0
## stitching_id: RCP Standard-RCP-4.5~hectorUI1~1
## recipe_location: data/stitched_CAMS-CSM1-0_tas_RCP Standard-RCP-4.5~hec...
## <xarray.DataArray 'tas' (time: 3000)>
## array([271.2429 , 272.0805 , 278.03003, ..., 282.83716, 275.08975, 273.1367 ],
## dtype=float32)
## Coordinates:
## * time (time) datetime64[ns] 1850-01-31 1850-02-28 ... 2099-12-31
## lat float64 38.69
## lon float64 256.5
## Attributes:
## units: K
## variable: tas
## experiment: historical
## ensemble: r2i1p1f1
## model: CAMS-CSM1-0
## stitching_id: RCP Standard-RCP-4.5~hectorUI1~1
## recipe_location: data/stitched_CAMS-CSM1-0_tas_RCP Standard-RCP-4.5~hec...
## Loading required package: spam## Spam version 2.9-1 (2022-08-07) is loaded.
## Type 'help( Spam)' or 'demo( spam)' for a short introduction
## and overview of this package.
## Help for individual functions is also obtained by adding the
## suffix '.spam' to the function name, e.g. 'help( chol.spam)'.##
## Attaching package: 'spam'## The following objects are masked from 'package:base':
##
## backsolve, forwardsolve## Loading required package: viridis## Loading required package: viridisLite##
## Try help(fields) to get started.
pr
#### python block ####
[pr_time_slice, pr_grid_slice] = select_time_grid_slice(varname='pr')Take a look at this data in R:
## <xarray.DataArray 'pr' (lat: 160, lon: 320)>
## array([[8.779846e-07, 1.044810e-06, 1.040847e-06, ..., 8.513659e-07,
## 8.526080e-07, 8.478734e-07],
## [9.318699e-07, 7.758846e-07, 9.415436e-07, ..., 3.580494e-07,
## 8.652287e-07, 7.310244e-07],
## [3.432085e-06, 4.102144e-06, 4.132919e-06, ..., 3.420937e-06,
## 3.185584e-06, 3.500774e-06],
## ...,
## [3.830799e-06, 3.803805e-06, 3.676216e-06, ..., 3.684879e-06,
## 3.910075e-06, 3.712560e-06],
## [1.227253e-06, 1.231288e-06, 1.297264e-06, ..., 1.111799e-06,
## 1.432357e-06, 8.047313e-07],
## [9.385178e-07, 9.380813e-07, 9.376278e-07, ..., 9.399242e-07,
## 9.392435e-07, 9.387159e-07]], dtype=float32)
## Coordinates:
## time datetime64[ns] 2100-12-31
## * lat (lat) float64 -89.14 -88.03 -86.91 -85.79 ... 86.91 88.03 89.14
## * lon (lon) float64 0.0 1.125 2.25 3.375 4.5 ... 355.5 356.6 357.8 358.9
## Attributes:
## units: kg m-2 s-1
## variable: pr
## experiment: historical
## ensemble: r2i1p1f1
## model: CAMS-CSM1-0
## stitching_id: RCP Standard-RCP-4.5~hectorUI1~1
## recipe_location: data/stitched_CAMS-CSM1-0_pr_RCP Standard-RCP-4.5~hect...
## <xarray.DataArray 'pr' (time: 3000)>
## array([7.364011e-06, 2.660703e-05, 8.277606e-06, ..., 3.328618e-05,
## 1.361743e-05, 1.056961e-05], dtype=float32)
## Coordinates:
## * time (time) datetime64[ns] 1850-01-31 1850-02-28 ... 2099-12-31
## lat float64 38.69
## lon float64 256.5
## Attributes:
## units: kg m-2 s-1
## variable: pr
## experiment: historical
## ensemble: r2i1p1f1
## model: CAMS-CSM1-0
## stitching_id: RCP Standard-RCP-4.5~hectorUI1~1
## recipe_location: data/stitched_CAMS-CSM1-0_pr_RCP Standard-RCP-4.5~hect...
